CGM-Based Prevention of Hypoglycemia Via Hypoglycemia Risk Assessment and Smooth Reduction of Insulin Delivery

ABSTRACT

An aspect of an embodiment or partial embodiment of the present invention (or combinations of various embodiments in whole or in part of the present invention) comprises, but not limited thereto, a method and system (and related computer program product) for continually assessing the risk of hypoglycemia for a patient and then determining what action to take based on that risk assessment. A further embodiment results in two outputs: (1) an attenuation factor to be applied to the insulin rate command sent to the pump (either via conventional therapy or via open or closed loop control) and/or (2) a red/yellow/green light hypoglycemia alarm providing to the patient an indication of the risk of hypoglycemia. The two outputs of the CPHS can be used in combination or individually.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims priority from U.S. ProvisionalApplication Ser. No. 61/155,357, filed Feb. 25, 2009, entitled “Method,System and Computer Program Product for CGM-Based Prevention ofHypoglycemia via Hypoglycemia Risk Assessment and Smooth ReductionInsulin Delivery,” U.S. Provisional Application Ser. No. 61/182,485,filed May 29, 2009, entitled “Method, System and Computer ProgramProduct for CGM-Based Prevention of Hypoglycemia via Hypoglycemia RiskAssessment and Smooth Reduction Insulin Delivery,” and U.S. ProvisionalApplication Ser. No. 61/263,932, filed Nov. 24, 2009, entitled “Method,System and Computer Program Product for CGM-Based Prevention ofHypoglycemia via Hypoglycemia Risk Assessment and Smooth ReductionInsulin Delivery,” of which all of the disclosures are herebyincorporated by reference herein in their entirety.

The present application is related to International Patent ApplicationSerial No. PCT/US2009/065725, filed Nov. 24, 2009, entitled “Method,System, and Computer Program Product for Tracking of Blood GlucoseVariability in Diabetes from Data,” the disclosure of which is herebyincorporated by reference herein in its entirety.

FIELD OF TILE INVENTION

Some aspects of some embodiments of this invention are in the field ofmedical methods, systems, and computer program products related tomanaging the treatment of diabetic subjects, more particularly toglycemic analysis and control. Some embodiments of the invention relateto means for preventing hypoglycemia in a subject with diabetes.

BACKGROUND OF THE INVENTION

Since the earliest use of insulin for treatment of diabetes, effortshave been made to adjust the dosages of insulin based on clinicalexperience, and more particularly, measurements of the level of glucose.Initially glucose tests were done infrequently and in a standardclinical laboratory. With the advent of intermittent self-monitoredglucose testing (i.e., self-monitoring blood glucose (SMBG)), suchtesting could be done by the patient and with a greater frequency at lowcost. The application of information derived from more frequent glucosetesting has allowed significantly better glucose control, and haslowered the occurrence of complications due to poor glycemic control.About a decade ago, the art incorporated continuous glucose monitors(i.e., continuous glucose monitoring (CGM)) that deliver glucosereadings every few minutes. The results were displayed to the patient,and variously provided indications of the trend of the glucose as wellas high-glucose and low-glucose alarms. Technological advances have beenmade also in the development of insulin pumps, which can replacemultiple daily self-injections of insulin. These currently availabledevices can deliver precise insulin dosages, typically on a programmableschedule which may be adjustable on the basis of input from the user orhealthcare professional, or on the basis of data from a continuousglucose monitor.

Basic algorithms have been developed that estimate an appropriateinsulin dosing schedule based, for example, on patient weight, and thesealgorithms provide a reasonable first approximation of a clinicallyappropriate insulin-dosing schedule. There is, however, considerablevariation among patients with regard to their metabolism andresponsiveness to insulin.

Various approaches have been applied to making calculations that usecontinuous glucose monitor (CGM) data to improve or adjust insulindosing. Artificial pancreas algorithms attempt to regulate blood glucoseconcentration in the face of meal disturbances and physical activity.

Other approaches, for example, provide for setting a basal insulin dosebased on consideration of a patient's history, particularly glucoseexcursion data over a period of time.

Nevertheless, in spite of current aspects of diabetes care management,tight glycemic control has yet to be achieved. Insulin pump shut-offalgorithms, as have been described in the prior art, use CGM data toinform the decision to completely stop the flow of insulin based on aprediction of hypoglycaemia. This approach has been shown to reduce therisk of nocturnal hypoglycaemia. A possible drawback is that the use ofan on-off control law for basal insulin, similar to bang-bang or relaycontrol, may induce undesired oscillations of plasma glucose. In fact,lithe basal insulin is higher than that needed to keep the glycemictarget, the recovery from hypoglycemia would be followed by applicationof the basal that will cause a new shut-off occurrence. The cycle ofshut-off interventions yields an insulin square wave that inducesperiodic oscillation of plasma glucose.

BRIEF SUMMARY OF THE INVENTION

An aspect of an embodiment of the present invention seeks to, amongother things, remedy the problems in the prior art. With theintroduction of subcutaneous continuous glucose monitoring (CGM) devicesthat provide nearly real time measurement there is a need for achievingtight glycemic control. An aspect of an embodiment of the presentinvention CGM-Based Prevention of Hypoglycemia System (CPHS) and relatedmethod disclosed here serves to, but not limited thereto, provide anindependent mechanism for mitigating the risk of hypoglycemia.Applications of this technology include, but not limited thereto,CGM-informed conventional insulin pump therapy, CGM-informed open-loopcontrol systems, and closed-loop control systems. These systems may bemost applicable to the treatment of Type 1 and Type 2 diabetes (T1DM andT2DM, respectively), but other applications are possible.

An aspect of an embodiment or partial embodiment of the presentinvention (or combinations of various embodiments in whole or in part ofthe present invention) comprises, but is not limited to, a method andsystem (and related computer program product) for continually assessingthe risk of hypoglycemia for a patient and then determining what actionto take based on that risk assessment. A further embodiment results intwo outputs: (1) an attenuation factor to be applied to the insulin ratecommand sent to the pump (either via conventional therapy or via open orclosed loop control) and/or (2) a red/yellow/green light hypoglycemiaalarm providing to the patient an indication of the risk ofhypoglycemia. The two outputs of the CPHS can be used in combination orindividually.

An aspect of an embodiment of the present invention innovates innumerous ways on existing technologies by acting on the risk ofhypoglycemia and not explicitly and exclusively on the glucose level. Anaspect of an embodiment of the invention further innovates by graduallydecreasing insulin levels, therefore avoiding under-insulinization ofthe patient and reducing the risk of hyperglycemia as compared to rigidpump shut-off algorithms. An aspect of an embodiment of the inventionalso uses insulin pump feedback to increase the accuracy of thehypoglycemia risk assessment. An aspect of an embodiment of theinvention further integrates an alert system that not only informs theuser that the system is actively preventing hypoglycemia but is alsocapable of requesting user intervention in case no amount of insulin.

An aspect of an embodiment of the CPHS (and related method) preventshypoglycemia, rather than merely manipulating 13G into a specific targetor tight range.

An aspect of an embodiment of the present invention provides a methodfor preventing or mitigating hypoglycemia in a subject. The method maycomprise the following: obtaining metabolic measurements associated withthe subject; continuously assessing a risk of hypoglycemia based on themetabolic measurements; and evaluating the risk of hypoglycemia todetermine one of the following outcomes 1) no action is needed, 2)attenuation of insulin delivery is needed, 3) additional intervention isneeded, or 3) attenuation of insulin delivery and additionalintervention are needed.

An aspect of an embodiment of the present invention provides a systemfor preventing or mitigating hypoglycemia in a subject. The system maycomprise the following: an obtaining device for obtaining metabolicmeasurements associated with the subject; an assessment device forcontinuously assessing a risk of hypoglycemia based on the metabolicmeasurements; and an evaluation device for evaluating the risk ofhypoglycemia to determine one of the following outcomes: 1) no action isneeded, 2) attenuation of insulin delivery is needed, 3) additionalintervention is needed, or 4) attenuation of insulin delivery andadditional intervention are needed.

An aspect of an embodiment of the present invention provides a computerprogram product comprising a computer useable medium having a computerprogram logic for enabling at least one processor in a computer systemto prevent or mitigate hypoglycemia in a subject. The computer logic maycomprise the following: obtaining data of metabolic measurementsassociated with the subject; continuously assessing a risk ofhypoglycemia based on the metabolic measurements; and evaluating therisk of hypoglycemia to determine one of the following outcomes: 1) noaction is needed, 2) attenuation of insulin delivery is needed 3)additional intervention is needed, or 4) attenuation of insulin deliveryand additional intervention are needed.

It should be appreciated that the continuous assessment may occur Xtimes per second, where 1<X<1000 (as well as at a faster rate orfrequency if desired or required). It should be appreciated that thecontinuous assessment may occur X times per hour, where 1<X<1000. Itshould be appreciated that the continuous assessment may occur X timesper day, where 1<X<1000. The assessment can be made periodically or attime intervals where their duration and frequency can vary. As anexample, the assessment may occur every minute or every few to severalminutes. Another example of continuous assessment shall include anypoint in time where a sample (for example, but not limited thereto, BG,CGM samples, glucose measurements, etc.) or input (for example, but notlimited thereto, basal rate change, bolus events acknowledged by thepump, etc.) is received that can be assessed. For instance, the riskassessment may be event driven. Also, it should be appreciated that agiven day(s) can be skipped for conducting assessment activities orsteps.

The foregoing and other objects, features and advantages of the presentinvention, as well as the invention itself, will be more fullyunderstood from the following description of preferred embodiments, whenread together with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated into and form a partof the instant specification, illustrate several aspects and embodimentsof the present invention and, together with the description herein,serve to explain the principles of the invention. The drawings areprovided only for the purpose of illustrating select embodiments of theinvention and are not to be construed as limiting the invention.

FIG. 1 schematically provides an exemplary embodiment of the CGM-basedprevention of hypoglycemia system (CMS).

FIG. 2 schematically provides an exemplary embodiment of the CGM-basedprevention of hypoglycemia system (CPHS).

FIG. 3 schematically provides a more detailed exemplary embodiment ofthe CGM-based prevention of hypoglycemia system (CPHS) front FIG. 2.

FIG. 4 schematically provides an exemplary embodiment of the CGM-basedprevention of hypoglycemia system (CPHS).

FIG. 5 schematically provides an exemplary embodiment of the CGM-basedprevention of hypoglycemia method, (and modules of a related system).

FIG. 6 schematically provides simulation results from an exemplaryembodiment of the CGM-based prevention of hypoglycemia system (CPHS).

FIG. 7 schematically provides simulation result's from an exemplaryembodiment of the CGM-based prevention of hypoglycemia system (CPHS).

FIG. 8 schematically provides simulation results from an exemplaryembodiment of the CGM-based prevention of hypoglycemia system (CPHS).

FIG. 9 schematically provides simulation results from an exemplaryembodiment of the CGM-based prevention of hypoglycemia system (CPHS).

FIG. 10 schematically provides simulation results from an exemplaryembodiment of the CGM-based prevention of hypoglycemia system (CPHS).

FIG. 11: provides a schematic block diagram of an aspect of anembodiment of the present invention relating processors, communicationslinks, and systems, for example.

FIG. 12: Provides a schematic block diagram of an aspect of anembodiment of the present invention relating processors, communicationslinks, and systems, for example.

FIG. 13: Provides a schematic block diagram of an aspect of anembodiment of the present invention relating processors, communicationlinks, and systems, for example.

FIG. 14: Provides a schematic block diagram for an aspect of a system orrelated method of an aspect of an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

An aspect of an embodiment of the CGM-Based Prevention of HypoglycemiaSystem (CPHS) (and related method and computer program product)presented here may utilize CGM data to continually assess the risk ofhypoglycemia for the patient and then provides two outputs: (1) anattenuation factor to be applied to the insulin rate command sent to thepump (either via conventional therapy or via open or closed loopcontrol) and/or (2) a red/yellow/green light hypoglycemia alarmproviding to the patient an indication of the risk of hypoglycemia. Thetwo outputs of the CPHS can be used in combination or individually.

The first section below presents the CPHS for the case where the onlyinput to the system is CGM data.

The second section presents the CPHS for the case where, in addition toCGM data, the system receives as an input some external data, includinginsulin commands.

A distinguishing aspect of an embodiment of the present inventionsystem, method and computer program product compared to other methods ofhypoglycemia prevention, for example, but not limited thereto is its useof formal assessments of hypoglycemia risk, both in determining theappropriate attenuation of insulin and in producing the appropriatered/yellow/green signal.

Another aspect of an embodiment of the present invention is theattenuation function of the CPHS (and related method and computerprogram product), which adjusts the restriction of insulin as a smoothfunction of CGM measures, not abruptly, as in prior art pump-shutoffmethods. In the following sections, a specific methodology based on arisk symmetrization function is presented. The same techniques could beused for other risk assessment techniques, including risk assessmentsthat use other input signals such as meal acknowledgement informationand indications of physical activity, as long as they vary smoothly as afunction of CGM data. No other hypoglycemia prevention system relies onthe use of risk assessments to produce a smoothly varying attenuationfactor.

Another aspect of an embodiment of the present invention system, methodand computer program product is the traffic signal abstraction for thehypoglycemia alarm system.

Before proceeding, it is important to note that conventional pumpshutoff methods suffer from the complexity of deciding exactly when toshut off and exactly when to resume operation, with both decisions beingsignificantly hampered by CGM noise and errors.

Smooth adjustment of the restriction of insulin, as in the CPHS,accommodates CGM noise in a natural way. First, if there are spuriouserrors in the CGM signal, they can only become spurious errors in thedegree of attenuation because there is never a point in time where acrisp attenuation decision has to be made. Next, systematic errors inthe CGM signal are eventually accommodated by the system. For example,even if the CGM is reading high (indicating a higher blood glucose thanis actually the case), a downward trend will eventually respond in asevere restriction of delivery of insulin.

CPHS with CGM Input Only

This section presents a basic form of an embodiment of the presentinvention in which only CGM data is used to prevent hypoglycemia, asillustrated in FIG. 1. It should be noted that the CPHS can functionwithout any other input signals. This subsection explains how the CPHSwould operate in a CGM-only configuration. Also included is anillustration of procedures by which the attenuation factor is computedand red/yellow/green light hypoglycemia alarms are generated (See FIG.2).

FIG. 1 illustrates a first exemplary embodiment of the hypoglycemiaprevention system 100. The subject, such as a patient 102 may be adiabetic subject who takes insulin to prevent complications arising fromdiabetes. Continuous Glucose Monitor (CGM) 104 collects informationabout the patient, specifically blood or interstitial glucose levels.The blood or interstitial glucose data is measured directly from thepatient 102, without the inclusion of any intermediary or independentdevice. CPHS 106 takes as input the blood glucose data acquired by CGM104. Based on this data, the CPHS 106 evaluates the risk ofhypoglycemia. The risk corresponds to one or more actions to be taken,including taking no action, attenuating insulin delivery, and/or takingadditional intervention. If the output of the CPHS 106 is to attenuateinsulin delivery, the CPHS indicates to the insulin delivery device 108to lower the amount of insulin delivered to the patient 102. It shouldbe appreciated that as discussed herein, a subject may be a human or anyanimal. It should be appreciated that an animal may be a variety of anyapplicable type, including, but not limited thereto, mammal,veterinarian animal, livestock animal or pet type animal, etc. As anexample, the animal may be a laboratory animal specifically selected tohave certain characteristics similar to human (e.g. rat, dog, pig,monkey), etc. It should be appreciated that the subject may be anyapplicable human patient, for example.

FIG. 2 illustrates a second exemplary embodiment of the hypoglycemiaprevention system 200. Again, a subject, such as a patient 202 is adiabetic subject and the CGM 204 collects information about the patient202. The CPHS 206 takes as input the blood glucose data acquired by CGM204. Based on this data, the CPHS 206 evaluates the risk of hypoglycemiaand determines whether and what kind of action to take. These actionsinclude taking no action, attenuating insulin delivery, and/or takingadditional intervention.

Depending on the risk of hypoglycemia, a visual indicator 210 displays acolored light. If there is no risk of hypoglycemia, the CPHS 206 willtake no action and the visual indicator 210 will present a green light(or other type of indicator as desired or required). If the risk ofhypoglycemia is low the CPHS 206 will attenuate insulin delivery and thevisual indicator 210 will present a yellow light (or other type ofindicator as desired or required). If the risk of hypoglycemia is high,the CPHS 206 will either (1) call for additional intervention, or (2)call for additional intervention and attenuate insulin delivery. Ineither ease, the visual indicator, 210, will present a red light (orother type of indicator as desired or required).

It should be appreciated that any of the embodiments discussed hereinmay be intended for some sort or kind of visual tracking. However, itshould be appreciated that information that is conveyed visually may beconveyed audibly and/or tactically (perceptible to the sense of touch)if desired or required. Accordingly, a audible and/or tactile schemewould be provided to convey or provide at least some or all of theaspects being conveyed visually or in combination therewith. Moreover,for example, audible signals may be provided in addition to or inconcert or parallel with the visual information.

FIG. 3 presents a more detailed view of the system illustrated in FIG.2. As in the previous figures, the subject or patient 302 CGM 304, andinsulin delivery device 306 are provided. The CPHS 308 uses CGM data,y(t), to compute an attenuation factor, φ_(brakes)(R(t)), based on arisk of hypoglycemia assessment, R(t). The CPHS 308 may also or solelypresent to the user red, yellow, or green lights indicating the risk ofhypoglycemia via visual indicator 310. The CPHS is designed to add asafety supervision function to different types of blood glucosemanagement functions, including conventional therapy, open-loop andadvisory mode systems, and closed loop systems. Keeping in mind that thesubject or patient has ultimate authority over insulin boluses, the CPHS306 serves to modify insulin rates by modifying the programmed rate ofinsulin injection, J_(command)(t), in the insulin delivery device 308.This attenuation of insulin delivery is performed by multiplying thehypoglycemia attenuation factor by the programmed rate of insulininjection to determine an actual rate of insulin injection:

J _(actual)(t)=φ_(brakes)(R(t))·J _(command)(t)

The attenuation factor output of the CGM-only CPHS is computed via analgorithmic process referred to as brakes. The brakes algorithm andmethod are designed to adjust insulin rate commands to the insulin pumpto avoid hypoglycemia. A feature of an embodiment of the presentinvention is that brake action smoothly attenuates the patient's insulindelivery rate at the present time t by monitoring CGM and insulin pumpdata, assessing a measure of the patient's future risk of hypoglycemiaR(t), and then computing an attenuation factor φ_(brakes)(R(t)). Theattenuation factor is computed as follows:

${\varphi_{brakes}\left( {R(t)} \right)} = \frac{1}{1 + {k \cdot {R(t)}}}$

where k is an aggressiveness parameter that may be adjusted to match thepatient's physiology (i.e. according to the patient's insulinsensitivity).

As illustrated in FIG. 3, the attenuation factor would be used by theinsulin delivery device 308 to compute reduced actual pump rateJ_(actual)(t)(U/hr) according to:

J _(actual)(t)=φ_(brakes)(R(t))·J _(command)(t)

where J_(actual)(t) is the attenuated insulin rate (U/hr) andJ_(command)(t) is the rate of insulin injection (U/hr) that the pump isset to administer.

In the CGM-only version of the CPHS, the risk assessment function R(t)is computed purely from CGM data, as follows. First, R(t) is computed asa sample average of raw risk values:

${R(t)} = {\frac{1}{M}{\sum\limits_{\tau = 0}^{M - 1}{\overset{\sim}{R}\left( {t - \tau} \right)}}}$

where M is the size of the moving average window for risk assessmentand, for any stage t, the raw risk value is computed as

${\overset{\sim}{R}(t)} = \left\{ \begin{matrix}{10 \cdot \left\lbrack {{\gamma (\theta)} \cdot \left( {{\ln \left( {y(t)} \right)}^{\alpha {(\theta)}} - {\beta (\theta)}} \right)} \right\rbrack^{2}} & {{{if}\mspace{14mu} 20} < {y(t)} < \theta} \\100 & {{{if}\mspace{14mu} {y(t)}} \leq 20} \\0 & {{otherwise}.}\end{matrix} \right.$

where y(t) (mg/dl) is either the most recent CGM sample or an average ofrecent CGM samples (e.g. moving average, exponentially weighted movingaverage, etc.) and the parameters α(θ), β(θ), and γ(θ) are computed inadvance based on a threshold glucose concentration, θ (mg/dl), which isspecific to the embodiment of the CPHS. Note that θ is the glucoseconcentration below which the risk function will be positive, resultingin an attenuation factor φ_(brakes)(R(t))<1.

Values for parameters α(θ), β(θ), and γ(θ) are listed for variousthreshold glucose concentrations, θ, in Table 1 below.

TABLE 1 Threshold Glucose Concentration θ (mg/dl) α(θ) β(θ) γ(θ) 900.384055 1.78181 12.2688 100 0.712949 2.97071 4.03173 112.5 1.084055.381 1.5088 120 1.29286 7.57332 0.918642 160 2.29837 41.8203 0.10767200 3.24386 223.357 0.0168006

The choice of values of k, M, and θ depends upon the embodiment of theCPHS. In some embodiments, these parameters will be fixed at presetvalues, with M typically being out to one for embodiments in which CGMvalues arrive frequently, say every minute. In other embodiments, k, M,and θ will be manually set to fixed values in concert with the patient'sphysician (e.g. according to the patient's insulin sensitivity andeating behavior) or input by the patient or other individual providingthe input. In yet other embodiments, the parameter values will be setaccording to regression formulas involving the patient's physicalcharacteristics (e.g. body weight, total daily insulin TDI (U),carbohydrate ratio, correction factor CF (mg/dl/U), age, etc.). One suchregression formula for k follows:

k=exp(−0.7672−0.0091·TDI)/+0.0449·CF)

Experiments run on the FDA-accepted T1DM simulator at the University ofVirginia show that the performance of the brakes varies smoothly as afunction of k and θ, and, while setting these parameters optimally leadsto the best ability to prevent hypoglycemia, adverse events do not arisewhen non-optimal values are chosen.

Testing was completed to determine the viability of this embodiment ofthe invention. The following results show the efficacy of the brakesalgorithm and methodology for the embodiment where k=1, M=1, and θ=120(mg/dl). The results are obtained from the FDA-accepted UVA/U. PadovaMetabolic Simulator. Some T1DM patients experience highly variableinsulin sensitivity (e.g. after physical activity), and, for such apatient, it can happen that his/her basal rate of insulin delivery,which is tuned to achieve fasting euglycemia under normal circumstances,is from time to time suddenly too high, putting the patient at risk ofhypoglycemia. For these patients, an embodiment of the CGM-only CPHSwith k=1, M=1, and θ=120 (mg/dl) will successfully mitigate the risk ofhypoglycemia, as illustrated in the simulation results of FIG. 6.

FIG. 6(A) involves 100 in silico patients with T1DM, using the UVA andU. Padova Metabolic Simulator. All 100 patients start at time t=0 with aglucose concentration of 1.50 mg/dl and are subjected at that time to anelevated basal rate J_(command)(t) that is two times what would berequired to achieve a fasting blood glucose of 112.5 mg/dl. Theexperiment is designed to reflect the situation where a patient'sinsulin sensitivity is greatly enhanced, say due exercise. Note that 46%of the patients experience blood glucose below 60 (mg/dl), and 88% ofthe patients experience blood glucose below 70 (mg/dl). The chartdemonstrates the minimum and maximum BG over the duration of theexperiment plotted on the on the X- and Y-axis, respectively, and thegraph indicates the BG (mg/dl) over time (hours).

FIG. 6(B) presents the simulation with an elevated basal rate withCGM-only brakes. Here, for the 2× basal rate scenario, CGM-only brakeswith k=1, M=1, and θ=120 (mg/dl) substantially reduces the occurrence ofhypoglycemia, with only 15% experiencing hypoglycemia below 60 (mg/dl),and only 39% of the population experiencing a blood glucose of 70(mg/dl). The chart demonstrates the minimum and maximum BG over theduration of the experiment plotted on the on the X- and Y-axis,respectively, and the graph indicates the BG (mg/dl) over time (hours).

As a complement to the attenuation function of the system above, theCPHS (and related method and computer program product) employs a newhypoglycemia alarm that provides a color-coded signal to the patientbased on the abstraction of a traffic light. In essence an embodiment ofthis system and related method will present a:

-   -   1. Green light to the patient whenever there is no risk of        hypoglycemia;    -   2. Yellow light to the patient whenever there is a risk of        hypoglycemia but hypoglycemia is not imminent and could be        handled by insulin attenuation; and    -   3. Red light to the patient whenever hypoglycemia is inevitable        regardless of the attenuation of the insulin pump.

In the CGM-only version of the alarm system, the method for determiningwhich signal to present is as follows:

-   -   1. R(t)=0 presents a green light;    -   2. R(t)>0 and y(t)≧K_(red) presents a yellow light; and    -   3. y(t)>K_(red) presents a red light.

The choice of the parameter K_(red) also depends upon the embodiment ofthe system. If 60 mg/dl is acknowledged as the onset of hypoglycemia,then K_(red) could be chosen as 65 mg/dl, so that the patient has theopportunity to administer rescue carbohydrates before the hypoglycemicthreshold is crossed. To avoid false alarms, it might be desirable as analternative to require y(t)<K_(red) for a specified amount of time (e.g.two minutes) before tripping the red light.

FIG. 5 illustrates an exemplary embodiment of the CGM-based preventionof hypoglycemia method and system. In an approach, in step 502 (or theapplicable system module or means) obtains metabolic measurements fromthe subject. Based on the metabolic measurements, step 504 (or theapplicable system module or means) includes continuously assessing therisk of hypoglycemia. Depending on the assessed risk of hypoglycemia,step 506 (or the applicable system module or means) includes evaluatingthe risk of hypoglycemia to determines what possible action to take.Possible actions (or their applicable system modules or means) mayinclude step 508-1, taking no action; step 508-2, attenuating insulindelivery; step 508-3, taking additional intervention; and step 508-4,attenuating insulin delivery and taking additional intervention.

CPHS with CGM and Insulin Pump Data

This section describes the CPHS for the case where, in addition to CGMdata, the system receives external data, including insulin pump data.Insulin pump data refers either to (1) commands from the user (inconventional therapy) or controller (in open- or closed-loop control) or(2) feedback from the pump regarding delivered insulin (regardless ofthe type of control employed). The method described here also extends toconfigurations where, in addition to CGM and insulin pump data, yetother inputs are available to the CPHS, including meal information,indications of physical activity, and heart rate information. Theinsulin pump data or other external input data are indirect metabolicmeasurements. These measurements are not collected directly from thepatient and are collected from other sources that can indicateinformation about the current patient state. For instance, insulin pumpdata is an indirect metabolic measurement. It should be appreciated thatan embodiment of the CPHS disclosed can take as inputs both directmetabolic measurements and indirect metabolic measurements. This generalsituation is depicted in FIG. 4. As before, the outputs of the system400 are: (1) an attenuation factor designed to restrict the delivery ofinsulin when there is significant risk of hypoglycemia and (2) ared/yellow/green light alarm system to inform the user of impendinghypoglycemia.

FIG. 4 presents an illustration of an enhanced hypoglycemia preventionsystem 400 including a CPHS, which uses CGM data and insulin pump data(associated with either conventional therapy or open or closed loopcontrol systems) to (1) compute an attenuation factor based on anassessment of the risk of hypoglycemia and/or (2) present to the userred, yellow, or green lights indicating the risk of hypoglycemia. Thesubject or patient, 402, is a diabetic subject and the CGM 404 collectsinformation about the patient. The CPHS 406 takes as input the bloodglucose data acquired by the CGM 404. Based on this data, the CPHS 406evaluates the risk of hypoglycemia and determines whether and what kindof action to take. These actions include taking no action, attenuatinginsulin delivery, and/or taking additional intervention. Depending onthe risk of hypoglycemia, the visual indicator 410, displays a coloredlight (or other indicator as desired or required). As in the previousembodiments, if there is no risk of hypoglycemia, the CPHS 406 will takeno action and the visual indicator 410 will present a green light. Ifthe risk of hypoglycemia is low the CPHS 406 will attenuate insulindelivery, and the visual indicator 410 will present a yellow light. Ifthe risk of hypoglycemia is high, the CPHS 406, will either (1) call foradditional intervention, or (2) call for additional intervention andattenuate insulin delivery. In either case, the visual indicator 410will present aced light.

When the CPHS (and related method and computer program product) hasaccess to other data in addition to CGM data, an embodiment of theinvention can correct the glucose signal used in the risk calculation.Here, the focus is on the case where, in addition to CGM data andpossibly other signals, the CMS has explicit access to insulin pump datacoming either in the form of (1) user inputs (i.e. commanded insulinrate at any time and insulin boluses whenever they occur) or (2)feedback from the pump regarding delivered insulin. The system isgeneric in that requests for insulin may come either from conventionaltherapy (with the patient in charge) or from open- or closed-loopcontrol. With the additional input data it is possible to compute acorrected glucose concentration y_(corrected)(t) (mg/dl); two methods ofcomputing y_(corrected)(t) are described in the paragraphs that follow.The corrected glucose reading y_(corrected)(t) is used to compute acorrected raw assessment of the risk of hypoglycemia {tilde over(R)}_(corrected)(t), as below:

${{\overset{\sim}{R}}_{corrected}(t)} = \left\{ \begin{matrix}{10 \cdot \left\lbrack {{\gamma (\theta)} \cdot \left( {{\ln \left( {y_{corrected}(t)} \right)}^{\alpha {(\theta)}} - {\beta (\theta)}} \right)} \right\rbrack^{2}} & {{{if}\mspace{14mu} 20} < {y_{corrected}(t)} < \theta} \\100 & {{{if}\mspace{14mu} {y_{corrected}(t)}} \leq 20} \\0 & {{otherwise}.}\end{matrix} \right.$

where, as before, the parameters α(θ), β(θ), and γ(θ) are computed inadvanced based on a threshold glucose concentration θ (mg/dl), which isspecific to the embodiment of the CPHS. Note that θ is the glucoseconcentration below which the risk function will be positive. Values forα(θ), β(θ), and γ(θ) are listed for different thresholds θ in Table 1.Finally, the corrected risk assessment R_(corrected)(t) (not raw) iscomputed as

${R_{corrected}(t)} = {\frac{1}{M}{\sum\limits_{\tau = 0}^{M - 1}{{\overset{\sim}{R}}_{corrected}\left( {t - \tau} \right)}}}$

where, as before, M is the size of the moving average window for riskassessment.

The corrected assessment of risk R_(corrected)(t) is used to compute apower brakes pump attenuation factor φ_(powerbrakes)(R_(corrected)(t),as follows:

${\varphi_{powerbrakes}\left( {R_{corrected}(t)} \right)} = \frac{1}{1 + {k \cdot {R_{corrected}(t)}}}$

where k is an aggressiveness parameter that may be adjusted to match thepatient's physiology (i.e. according to the patient's insulinsensitivity). As illustrated in FIG. 4, the attenuation factor would beused by the insulin delivery device to compute reduced actual pump rateJ_(actual)(t) (U/hr) according to:

J _(actual)(t)=φ_(powerbrakes)(R _(corrected)(t))·J _(command)(t)

where J_(command)(t) is the rate of insulin injection (U/hr) that thepump is set to administer, J_(actual)(t) is the attenuated insulin rate(U/hr). Thus, as with the brakes, the power brakes algorithm is designedto smoothly adjust insulin rate commands to the insulin pump to ovoidhypoglycemia.

As with the CGM-only brakes, the aggressiveness parameter in someembodiments will be set as k=1, M=1, and the threshold θ will be set tothe nominal value of 112.5 (mg/dl). In other embodiments, the parametersk, M, and θ will be manually set to other fixed values in concert withthe patient's physician (e.g. according to the patient's insulinsensitivity and eating behavior) or input by the patient or otherindividual providing the input.

In yet other embodiments, the parameters k, M, and θ will be setaccording to regression formulas involving the patient's physicalcharacteristics (e.g. body weight, total daily insulin TDI (U),carbohydrate ratio, correction factor CF (mg/dl/U), age, etc.). One suchregression formula for k follows:

k=exp(−0.7672−0.0091·TDI+0.0449·CF).

Experiments run on the FDA-accepted T1DM simulator at the University ofVirginia show that the performance of the brakes varies smoothly as afunction of k, M, and θ, and, while setting these parameters optimallyleads to the best ability to prevent hypoglycemia, adverse events do notarise when non-optimal values are chosen.

Two methods are disclosed for computing a corrected glucose level. Thefirst method of computing corrected glucose involves the use of ametabolic state observer, which in turn (1) requires a model of bloodglucose-insulin dynamics and (2) requires knowledge of insulin pumpcommands and ingested carbohydrates. x(t) denotes a vector of metabolicstates associated with the patient, representing things likeinterstitial glucose concentration, plasma glucose concentration,insulin concentrations, contents of the gut, etc. {circumflex over(x)}(i) denotes the estimate of x(t) using all available input data upto time t, based on a linear state space model expressed generically as

x(t)=Ax(t−1)+Bu(t−1)+Gw(t−1),

where u(t) represents insulin inputs into the body and w(t) representsingested carbohydrates. The corrected glucose reading is computedaccording to,

y _(corrected)(t)=C{circumflex over (x)} _(τ)(t),

where C is a matrix that relates the metabolic state vector to measuredglucose, τ is a nonnegative integer parameter, and

{circumflex over (x)} _(τ) =A ^(τ) {circumflex over(x)}(t)+A(τ)Bu(t)+A(τ)Gw(t)

where A^(τ) is the A matrix of the state space model raised to the r-thpower and

${A(\tau)} = \left\{ \begin{matrix}0 & {{{if}{\mspace{11mu} \;}\tau} = 0} \\{\sum\limits_{s = 0}^{\tau - 1}A^{s}} & {{{if}\mspace{14mu} \tau} > 0.}\end{matrix} \right.$

In this method of computing y_(corrected)(t), the state space model(A,B,G,C), the state observer giving the estimate {circumflex over(x)}(t), and the parameter are all specific to the embodiment of theinvention.

The choice of τ depends upon the embodiment of the system. τ=0corresponds to assessing risk based on the best estimate of bloodglucose based on all of the data received up to time t. τ>0 correspondsto an assessment of the future risk of hypoglycemia, giving power brakesthe opportunity to intervene well before the onset of hypoglycemia,improving the chance that hypoglycemia can be avoided. An importantbenefit of an embodiment of the power brakes is that as soon asanticipated blood glucose reaches 110 mg/dl the attenuation-affect isrelease (sooner than would be the case with just brakes). In someembodiments, can be allowed to vary. For example, if the patient isunwilling unable to provide detailed information about meal content(making it difficult to predict future blood sugar), it may be desirableto adjust τ in the time frame after meals, as follows:

$\tau = \left\{ \begin{matrix}{0,} & {{{{{if}\mspace{14mu} t} - t_{meal}} < 60},} \\{30,} & {{otherwise},}\end{matrix} \right.$

where t_(meal) represents the time of the most recent meal.

The second method of computing y_(corrected)(t) involves the use of thepatient's correction factor CF (used in computing appropriate correctionboluses in conventional therapy) and requires knowledge of the amount ofactive correction insulin i_(correction)(t) (U) in the patient's body attime t, which can be obtained from standard methods of computing insulinon board. The formula for y_(corrected)(t) in this case is

y _(corrected)(t)=α·(y(t)−CF·i _(correction)(t))+(1−α)·y(t)

where α is an embodiment-specific parameter chosen in the unit interval[0, 1] and y(t) is the most recent CGM sample (or moving average ofrecent CGM samples).

Testing was completed to determine the viability of this embodiment ofthe invention. The following results show the efficacy of the powerbrakes algorithm, technique and methodology using the first method ofcomputing corrected glucose for y_(corrected)(t). A population-averagemodel was used for glucose-insulin kinetics, as described by the vectordifference equation:

x(t)=Ax(t−1)+Bu(t−1)+Gω(t−1)

where t is a discrete time index with the interval from t to t+1corresponding to one minute of real time and

-   -   1. x(t)=(∂G(t) ∂X(t) ∂I_(sc1)(t) ∂I_(sc2)(t) ∂I_(p)(t)        ∂G_(sc)(t) ∂Q₁(t) ∂Q₂(t))^(T) is a vector of state variables        referring to:        -   a. blood glucose: ∂G(t)=G(t)−G_(ref), where G(t) mg/dl is            blood glucose concentration at minute t and G_(ref)=112.5            (mg/dl) is a reference value for blood glucose;        -   b. remote compartment insulin action: ∂X(t)=X(t)−X_(ref)            where X(t) (min⁻¹) represents the action of insulin in the            remote compartment and X_(ref)=0 (min⁻¹) is a reference            value;    -   c. interstitial insulin, first compartment:        ∂I_(sc1)(t)=I_(sc1)(t)−I_(sc1,ref), where I_(sc1)(t) (mU) is        insulin stored in the first of two interstitial compartments and        I_(sc1,ref)=1.2949×10³ (mU) is a reference value;    -   d. interstitial insulin, second compartment:        ∂I_(sc2)(t)=I_(sc2)(t)−I_(sc2,ref), where I_(sc2)(t) (mU) is        insulin stored in the first of two interstitial compartments and        I_(sc2,ref)=1.2949×10³ (mU) is a reference value;        -   e. plasma insulin: ∂I_(p)(t)=I_(p)(t)−I_(p,ref), where            I_(p)(t) (mU) is plasma insulin and I_(p,ref)=111.2009            (Intl) is a reference value;        -   f. interstitial glucose concentration:            ∂G_(sc)(t)=G_(sc)(t)−G_(sc,ref), where G_(sc)(t) (mg/dl) is            the concentration of glucose in interstitial fluids, and            G_(sc,ref)=112.5 (mg/dl) is a reference value;        -   g. gut compartment 1: ∂Q₁(t)=Q₁(t)−Q_(1,ref), where Q₁(t)            (mg) is glucose stored in the first of two gut compartments            and Q_(1,ref)=0 (mg) is a reference value; and        -   h. gut compartment 2: ∂Q₂(t)=Q₂(t)−Q_(2,ref), where Q₂(t)            (mg) is glucose stored in the first of two gut compartments            and Q_(2,ref)=0 (mg) is a reference value.    -   2. u(t)=J_(command)(t)−J_(basal)(t) (mU/min) is the insulin        differential control signal at time t, where J_(command)(t)        (mU/min) is the current rate of insulin infusion and        J_(basal)(t) (mU/min) is the patient's normal/average basal rate        at time t.    -   3. ω(t)=meal(t)−meal_(ref) (mg/min) is the ingested glucose        disturbance signal at time t, where meal(t) is the rate of        glucose ingestion and meal_(ref)=0 (mg/min) is a reference meal        input value.    -   4. the state space matrices A, B, and G are

$A = \begin{bmatrix}{.9913} & {- 102.7} & {{- 1.50} \times 10^{- 8}} & {{- 2.89} \times 10^{- 6}} & {{- 4.1} \times 10^{- 4}} & 0 & {2.01 \times 10^{- 6}} & {4.30 \times 10^{- 5}} \\0 & {.839} & {5.23 \times 10^{- 10}} & {7.44 \times 10^{- 8}} & {6.84 \times 10^{- 6}} & 0 & 0 & 0 \\0 & 0 & {.9798} & 0 & 0 & 0 & 0 & 0 \\0 & 0 & {.0200} & {.9798} & 0 & 0 & 0 & 0 \\0 & 0 & {1.9 \times 10^{- 4}} & {.0180} & {.7882} & 0 & 0 & 0 \\{.0865} & {- 4.667} & {{- 2.73} \times 10^{- 10}} & {{- 6.59} \times 10^{- 8}} & {{- 1.26} \times 10^{- 5}} & {.9131} & {6.00 \times 10^{- 8}} & {1.90 \times 10^{- 6}} \\0 & 0 & 0 & 0 & 0 & 0 & {.9083} & 0 \\0 & 0 & 0 & 0 & 0 & 0 & {.09115} & {.9891}\end{bmatrix}$ $B^{T} = \begin{bmatrix}{{- 3.05} \times 10^{- 9}} & {1.34 \times 10^{- 10}} & {.9900} & {.0100} & {6.50 \times 10^{- 5}} & {{- 4.61} \times 10^{- 11}} & 0 & 0\end{bmatrix}$ $G^{T} = \begin{bmatrix}{6.76 \times 10^{- 7}} & 0 & 0 & 0 & 0 & {1.52 \times 10^{- 8}} & {.9534} & 0.0464\end{bmatrix}$

Estimates {circumflex over (x)}(t) of x(t) are computed based onknowledge of infused insulin u(t) and CGM measurements y(t) (mg/dl). Themeasurement signal can be modeled as follows:

y(t)−G _(ref) =Cx(t)−(v(t)

where v(t) (mg/dl) represents CGM signal noise and the state spacematrix C is

C ^(T)=[1 0 0 0 0 0 0 0]

The metabolic state observer is derived from the state space model forx(t) and y(t) as a Kalman filter, treating the meal disturbance processω(t) and the noise process v(t) as zero-mean, white, Gaussian processeswith covariances R=k₁=0.01 and Q=k₂=0.00001 respectively. Even thoughmeals ω(t) and sensor noise v(t) are not zero-mean, white, Gaussianprocesses in reality, the resulting Kalman filter is still a stablestate observer.

Some T1DM patients or subjects experience highly variable insulinsensitivity (e.g. after physical activity). For such a patient, it canhappen that his/her basal rate of insulin delivery, which is tuned toachieve fasting euglycemia under normal circumstances, is occasionallysuddenly too high, putting the patient at risk of hypoglycemia. Forthese patients, the power brakes with k=1 and θ=120 (mg/dl) willsuccessfully mitigate the risk of hypoglycemia, as illustrated in thesimulation results of FIG. 7.

Turning to FIG. 7, as in FIG. 6, this simulation experiment involves 100in silico patients with T1DM, using the UVA and U. Padova MetabolicSimulator. All 100 patients start at time=0 with a glucose concentrationof 150 mg/dl and are subjected at that time to an elevated basal rateJ_(command)(t) that is two times what would be required to achieve afasting blood glucose of 112.5 mg/dl. Recall from FIG. 6 that 46% of thepatients experience blood glucose below 60 (mg/dl), and 88% of thepatients experience blood glucose below 70 (mg/dl). FIG. 7(A)illustrates an elevated basal scenario using power brakes with k=1, M=1,θ=120 (mg/dl), and τ=0 (minutes). The chart demonstrates the minimum andmaximum BG over the duration of the experiment plotted on the on the X-and Y-axis, respectively, and the graph indicates the BG (mg/dl) overtime (hours). In this case, the power brakes compute the risk assessmentusing just the current best estimate of the patient's blood glucose(i.e. τ=0) based on all available information. Note that only 12% of thepatients experience blood glucose below 60 mg/dl and only 33% of thepatients experience blood glucose below 70 (mg/dl). FIG. 7(B)illustrates an elevated basal scenario using power brakes with k=1,θ=120 (mg/dl), and τ=30 (minutes). Here, for the 2× basal rate scenario,CGM-only brakes with k=1, M=1, θ=120 (mg/dl) substantially reduce theoccurrence of hypoglycemia, with 15% experiencing hypoglycemia below 60(mg/dl), and only 39% of the population experiencing a blood glucose of70 mg/dl. The chart demonstrates the minimum and maximum BG over theduration of the experiment plotted on the on the X- and Y-axis,respectively, and the graph indicates the BG (mg/dl) over time (hours).

Patients often administer pre-meal insulin boluses in anticipation ofthe meal that they are about to take. In unusual circumstances, thepatient may forget or otherwise be unable to eat the anticipated meal,and, of course, this puts the patient at severe risk of hypoglycemia.For these patients, the power brakes can act to reduce basal insulin soas to substantially reduce the incidence of hypoglycemia, as illustratedin FIGS. 8 and 9.

FIG. 8 is a simulation experiment involving 100 in silico patients withT1DM, using the UVA and U. Padova Metabolic Simulator. All 100 patientsstart at time t=0 with a glucose concentration of 150 mg/dl and aresubjected a meal bolus at hour 3; all 100 patients skip the intendedmeal and hold their basal rate of insulin delivery J_(command)(t) atwhat would be required to maintain a fasting blood glucose of 112.5mg/dl. Note that because the carbohydrates of the meal never arrive, allpatients experience a severe drop in blood glucose. 53% of the patientsexperience blood glucose below 60 (mg/dl); 90% experience blood glucosebelow 70 (mg/dl). The chart demonstrates the minimum and maximum BG overthe duration of the experiment plotted on the on the X- and Y-axis,respectively, and the graph indicates the BG (mg/dl) over time (hours).

FIG. 9 is an illustration of an embodiment of the invention, implementedin the simulator. As in FIG. 8, all 100 patients start at time t=0 witha glucose concentration of 150 mg/dl and are subjected a meal bolus athour 3; all 100 patients skip the intended meal and hold their basalrate of insulin delivery J_(command)(t) at what would be required tomaintain a fasting blood glucose of 112.5 mg/dl. FIG. 9(A) presents thepower brakes embodiment with k=1, θ=120 (mg/dl), and τ=0 (minutes). Withthe power brakes (τ=0), 46% of the patients experience blood glucosebelow 60 (mg/dl); only 88% of the patients experience blood glucosebelow 70 (mg/dl). The chart demonstrates the minimum and maximum BG overthe duration of the experiment plotted on the on the X- and Y-axis,respectively, and the graph indicates the BG (mg/dl) over time (hours).FIG. 9(B) presents the power brakes embodiment with k=1, M=1, and θ=120(mg/dl), and =30 (minutes). Here, the power brakes with τ=30 minutes,give a very substantial improvement in preventing hypoglycemia: only 10%of the patients experience blood glucose below 60 (mg/dl); only 41% ofthe patients experience blood glucose below 70 (mg/dl). The chartdemonstrates the minimum and maximum BG over the duration of theexperiment plotted on the on the X- and Y-axis respectively, and thegraph indicates the BG (mg/dl) over time (hours).

An embodiment of the CPHS (and related method and computer programproduct) with Insulin Input Commands, as illustrated in FIG. 4, uses anew hypoglycemia alarm system that provides a color-coded signal to thepatient based on the abstraction of a traffic light, augmenting thehypoglycemia prevention capabilities of the power brakes themselves. Inessence an embodiment of this system will present a:

-   -   1. Green light to the patient whenever there is no risk of        hypoglycemia;    -   2. Yellow light to the patient whenever there is a risk of        hypoglycemia but hypoglycemia is not imminent and could be        handled by insulin attenuation; and    -   3. Red light to the patient whenever hypoglycemia is inevitable        regardless of the attenuation of the insulin pump.

Having access to additional information (besides just CGM data), theRed/Yellow/Green. Light Hypoglycemia Alarm System, uses the correctedmeasurement value y_(corrected)(t) and the corrected risk functionR_(corrected)(t) as a principle means of determining what signal topresent:

-   -   1. R_(corrected)(t)=0 presents a green light;    -   2. R_(corrected) (t)>0 and y_(corrected,OFF)(t)≧K_(red) presents        a yellow light; and    -   3. y_(corrected,OFF)>K_(red) presents a red light,        where y_(corrected,OFF)(t) is an assessment of anticipated blood        glucose concentration given that the insulin pump is completely        shut down.

The choice of the parameter K_(red) also depends upon the embodiment ofthe system. If 60 mg/dl is acknowledged as the onset of hypoglycemia,then K_(red) could be chosen as 65 mg/dl, so that the patient has theopportunity to administer rescue carbohydrates before the hypoglycemicthreshold is crossed. To avoid false alarms, it might be desirable as analternative to require BG_(off)(t+σ|t)<K_(red) for a specified amount oftime (e.g. two minutes) before tripping the red light.

Building on the infrastructure for computing y_(corrected)(t) in thepower brakes, it is possible to compute y_(corrected,OFF)(t) as

y _(corrected,OFF)(t)=C{circumflex over (x)} _(σ,OFF)(t),

where σ is a nonnegative integer parameter, and

{circumflex over (x)} _(σ,OFF)(t)=A ^(τ) {circumflex over (x)}(t)+A(τ)Bu_(OFF)(t)+A(τ)Gw(t)

where {circumflex over (x)}(t) is the current estimate of the patient'smetabolic state and u_(OFF)(t) is input signal corresponding to theinsulin pump being completely shut down.

As with τ in the computation of y_(corrected)(t), the value of σ isspecific to the embodiment of the invention. Note that σ>0 correspondsto the anticipated value of blood glucose assuming that no more insulinis delivered.

A second method of computing y_(corrected,OFF)(t) corresponds to secondmethod of computing y_(corrected)(t) described above. In this case,

y _(corrected,OFF)(t)=y(t)−CF·i _(correction)(t)

where y(t) is the most recent CGM sample (or moving average of recentCGM samples) and CF and i_(correction)(t) are as they were above.

An exemplary embodiment of the Red/Yellow/Green Light Hypoglycemia AlarmSystem is now presented. Relevant parameters are as follows:

-   -   1. Red Light Alarm Parameters: K_(red)=80 (mg/dl) and τ=15        (Minutes);    -   2. Yellow Light Alarm. Parameters: θ=112.5 (mg/dl), and τ=15        (minutes); and    -   3. No pump attenuation, so that even when R(t)=0 the actual rate        of insulin infusion is equal to commanded insulin.

FIG. 10 shows results from the UVA/U. Padova Metabolic Simulator for 100adult Type 1 in silico patients, with basal rates of insulin deliveryset to be twice their fasting levels. With elevated basal rates, all 100patients eventually become hypoglycemic (by crossing 60 (mg/dl)). Notethat on average the yellow light turns on 118 minutes beforehypoglycemia and the red light tarns on 34 minutes before hypoglycemia.The plot shows the transition from green to yellow to red for arepresentative subject. The plot demonstrates BG, mg/dl, on the Y-axisand time, minutes, on the X-axis.

FIGS. 11-13 show block diagrammatic representations of aspects ofexemplary embodiments of the present invention. Referring to FIG. 11,there is shown a block diagrammatic representation of the system 1110essentially comprises the glucose meter 1128 used by a patient 1112 forrecording, inter alia, insulin dosage readings and measured bloodglucose (“BG”) levels. Data obtained by the glucose meter 1128 ispreferably transferred through appropriate communication links 1114 ordata modem 1132 to a processor, processing station or chip 1140, such asa personal computer, PDA, netbook, laptop, or cellular telephone, or viaappropriate Internet portal. For instance data stored may be storedwithin the glucose meter 1128 and may be directly downloaded into thepersonal computer or processor 1140 (or PDA, netbook, laptop, etc.)through an appropriate interface cable and then transmitted via theInternet to a processing location. It should be appreciated that theglucose meter 1128 and any of the computer processing modules or storagemodules may be integral within a single housing or provided in separatehousings. The communication link 1114 may be hardwired or wireless.Examples of hardwired may include, but not limited thereto, cable, wire,fiber optic, and/or telephone wire. Examples of wireless may include,but not limited thereto, Bluetooth, cellular phone link, RF link, and/orinfrared link. The modules and components of FIGS. 11-13 may betransmitted to the appropriate or desired computer networks (1152, 1252,1352) in various locations and sites. The modules and components of FIG.11 may be transmitted to the appropriate or desired computer networks1152 in various locations and sites (local and/or remote) via desired orrequired communication links 1114. Moreover, an ancillary orintervention device(s) or system(s) 1154 may be in communication withthe patient as well as the glucose meter and any of the other modulesand components shown in FIG. 11. Examples of ancillary device(s) andsystem(s) may include, but not necessarily limited thereto, anycombination of one or more of the following: insulin pump, artificialpancreas, insulin device, pulse oximetry sensor, blood pressure sensor,ICP sensor, EMG sensor, EKG sensor, ECG sensor, ECC sensor, pace maker,and heart rate sensor, needle, ultrasound device, or subcutaneous device(as well as any other biometric sensor or device). It should beappreciated that the ancillary or intervention device(s) or system(s)1154 and glucose meter 1128 may be any sort of physiological orbiological communication with the patients (i.e., subject). Thisphysiological or biological communication may be direct or indirect. Anindirect communication (which should not to be confused with an“indirect measurement” as discussed and claimed herein) may include, butnot limited thereto, a sample of blood or other biological fluids, orinsulin data. A direct communication (which should not to be confusedwith a “direct measurement” as discussed and claimed herein) may includeblood glucose (BG) data.

The glucose meter is common in the industry and includes essentially anydevice that can function as a BG acquisition mechanism. The BG meter oracquisition mechanism, device, tool or system includes variousconventional methods directed towards drawing a blood sample (e.g. byfingerprick) for each test, and a determination of the glucose levelusing an instrument that reads glucose concentrations byelectromechanical methods. Recently, various methods for determining theconcentration of blood analytes without drawing blood have beendeveloped. For example, U.S. Pat. No. 5,267,152 to Yang et al. (herebyincorporated by reference) describes a noninvasive technique ofmeasuring blood glucose concentration using near-IR radiationdiffuse-reflection laser spectroscopy. Similar near-IR spectrometricdevices are also described in U.S. Pat. No. 5,086,229 to Rosenthal etal. and U.S. Pat. No. 4,975,581 to Robinson et al. (of which are herebyincorporated by reference).

U.S. Pat. No. 5,139,023 to Stanley (hereby incorporated by reference)describes a transdermal blood glucose monitoring apparatus that relieson a permeability enhancer (e.g., a bile salt) to facilitate transdermalmovement of glucose along a concentration gradient established betweeninterstitial fluid and a receiving medium. U.S. Pat. No. 5,036,861 toSembrowich (hereby incorporated by reference) describes a passiveglucose monitor that collects perspiration through a skin patch, where acholinergic agent is used to stimulate perspiration secretion from theeccrine sweat gland. Similar perspiration collection devices aredescribed in U.S. Pat. No. 5,076,273 to Schoendorfer and U.S. Pat. No.5,140,985 to Schroeder (of which are hereby incorporated by reference).

In addition, U.S. Pat. No. 5,279,543 to Glikfeld (hereby incorporated byreference) describes the use of iontophoresis to noninvasively sample asubstance through skin into a receptacle on the skin surface. Glikfeldteaches that this sampling procedure can be coupled with aglucose-specific biosensor or glucose-specific electrodes in order tomonitor blood glucose. Moreover, International Publication No. WO96/00110 to Tamada (hereby incorporated by reference) describes aniotophoretic apparatus for transdermal, monitoring of a targetsubstance, wherein an iotophoretic electrode is used to move an analyteinto a collection reservoir and a biosensor is used to detect the targetanalyte present in the reservoir. Finally, U.S. Pat. No. 6,144,869 toBerner (hereby incorporated by reference) describes a sampling systemfor measuring the concentration of an analyte present.

Further yet, the BG meter or acquisition mechanism may includeindwelling catheters and subcutaneous tissue fluid sampling.

The computer, processor or PDA 1140 may include the software andhardware necessary to process, analyze and interpret the self-recordedor automatically recorded by a clinical assistant device diabetespatient data in accordance with predefined flow sequences and generatean appropriate data interpretation output. The results of the dataanalysis and interpretation performed upon the stored patient data bythe computer or processor 1140 may be displayed in the form of a paperreport generated through a printer associated with the personal computeror processor 1140. Alternatively, the results of the data interpretationprocedure may be directly displayed on a video display unit associatedwith the computer or processor 1140. The results additionally may bedisplayed on a digital or analog display device. The personal computeror processor 1140 may transfer data to a healthcare provider computer1138 through a communication network 1136. The data transferred throughcommunications network 1136 may include the self-recorded or automatedclinical assistant device diabetes patient data or the results of thedata interpretation procedure.

FIG. 12 shows a block diagrammatic representation of an alternativeembodiment having a diabetes management system that is apatient-operated apparatus or clinical-operated apparatus 1210 having ahousing preferably sufficiently compact to enable apparatus 1210 to behand-held and carried by a patient. A strip guide for receiving a bloodglucose test strip (not shown) is located on a surface of housing 1216.Test strip receives a blood sample from the patient 1212. The apparatusmay include a microprocessor 1222 and a memory 1224 connected tomicroprocessor 1222. Microprocessor 1222 is designed to execute acomputer program stored in memory 1224 to perform the variouscalculations and control functions as discussed in greater detail above.A keypad 1216 may be connected to microprocessor 1222 through a standardkeypad decoder 1226. Display 1214 may be connected to microprocessor1222 through a display driver 1230. Display 1214 may be digital and/oranalog. Speaker 1254 and a clock 1256 also may be connected tomicroprocessor 1222. Speaker 1254 operates under the control ofmicroprocessor 1222 to emit audible tones alerting the patient topossible future hypoglycemic or hyperglycemic risks. Clock 1256 suppliesthe current date and time to microprocessor 1222. Any displays may bevisual as well as adapted to be audible.

Memory 1224 also stores blood glucose values of the patient 1212, theinsulin dose values, the insulin types, and the parameters used by themicroprocessor 1222 to calculate future blood glucose values,supplemental insulin doses, and carbohydrate supplements. Each bloodglucose value and insulin dose value may be stored in memory 1224 with acorresponding date and time. Memory 1224 is may be a non-volatilememory, such as an electrically erasable read only memory (EEPROM).

Apparatus 1210 may also include a blood glucose meter 1228 connected tomicroprocessor 1222. Glucose meter 1228 may be designed to measure bloodsamples received on blood glucose test strips and to produce bloodglucose values from measurements of the blood samples. As mentionedpreviously, such glucose meters are well known in the art. Glucose meter1228 is preferably of the type which produces digital values which areoutput directly to microprocessor 1222. Alternatively, blood glucosemeter 1228 may be of the type which produces analog values. In thisalternative embodiment, blood glucose meter 1228 is connected tomicroprocessor 1222 through an analog to digital converter (not shown).

Apparatus 1210 may further include an input/output port 1234, such as aserial port, which is connected to microprocessor 1222. Port 1234 may beconnected to a modem 1232 by an interface, such as a standard RS232interface. Modem 1232 is for establishing a communication link 1248between apparatus 1210 and a personal computer 1240 or a healthcareprovider computer 1238 through a communication link 1248. The modulesand components of FIG. 12 may be transmitted to the appropriate ordesired computer networks 1252 in various locations and sites (localand/or remote) via desired or required communication links 1248.Moreover, an ancillary or intervention device(s) or system(s) 1254 maybe in communication with the patient as well as the glucose meter andany of the other modules and components shown in FIG. 12. Examples ofancillary device(s) and system(s) may include, but not necessarilylimited thereto any combination of one or more of the following: insulinpump, artificial pancreas, insulin device, pulse oximetry sensor, bloodpressure sensor, ICP sensor, EMG sensor, EKG sensor, ECG sensor, ECCsensor, pace maker, heart rate sensor, needle, ultrasound device, orsubcutaneous device (as well as any other biometric sensor or device).It should be appreciated that the ancillary or intervention device(s) orsystem(s) 1254 and glucose meter 1228 may be any sort of physiologicalor biological communication with the patients (i.e., subject). Thisphysiological or biological communication may be direct or indirect. Anindirect communication may include, but not limited thereto, a sample ofblood or other biological fluids. Specific techniques for connectingelectronic devices, systems and software through connections, hardwiredor wireless, are well known in the art. Another alternative example is“Bluetooth” technology communication.

Alternatively, FIG. 13 shows a block diagrammatic representation of analternative embodiment having a diabetes management system that is apatient-operated apparatus 1310, similar to the apparatus as shown inFIG. 12, having a housing preferably sufficiently compact to enable theapparatus 1310 to be, hand-held and carried by a patient. For example, aseparate or detachable glucose meter or BG acquisition mechanism/module1328. The modules and components of FIG. 13 may be transmitted to theappropriate or desired computer networks 1352 in various locations andsites (local and/or remote) via desired or required communication links1336. Moreover, an ancillary or intervention device(s) or system(s) 1354may be in communication with the patient as well as the glucose meterand any of the other modules and components shown in FIG. 13. Examplesof ancillary device(s) and system(s) may include, but not necessarilylimited thereto any combination of one or more of the following: insulinpump, artificial pancreas, insulin device, pulse oximetry sensor, bloodpressure sensor, ICP sensor, EMG sensor, EKG sensor, ECG sensor, ECCsensor, pace maker, heart rate sensor needle, ultrasound device, orsubcutaneous device (as well as any other biometric sensor or device).It should be appreciated that the ancillary or intervention device(s) orsystem(s) 1354 and glucose meter 1328 may be any sort of physiologicalor biological communication with the patients (i.e., subject). Thisphysiological or biological communication may be direct or indirect. Anindirect communication may include, but not limited thereto, a sample ofblood or other biological fluids. There are already self-monitoringdevices that are capable of directly computing the algorithms disclosedin this application and displaying the results to the patient withouttransmitting the data to anything else. Examples of such devices areULTRA SMART by LifeScan, Inc., Milpitas, Calif. and FREESTYLE TRACKER byTherasense, Alameda, Calif.

It should be appreciated that the various blood glucose meters, systems,method and computer program products discussed herein are applicable forCGM. Accordingly, various blood glucose meters, systems, and methods maybe utilized with the various embodiments of the present invention. Forexample, CGM devices may include: Guardian and Paradigm from Medtronic;Freestyle navigator (Abbott Diabetes Care); and Dexcom Seven fromDexcom, Inc., or other available CGM devices.

Accordingly, the embodiments described herein are capable of beingimplemented over data communication networks such as the internet,making evaluations, estimates, and information accessible to anyprocessor or computer at any remote location, as depicted in FIGS. 11-13and/or U.S. Pat. No. 5,851,186 to Wood, of which is hereby incorporatedby reference herein. Alternatively, patients located at remote locationsmay have the BG data transmitted to a central healthcare provider orresidence, or a different remote location.

It should be appreciated that any of the components/modules discussed inFIGS. 11-13 may be integrally contained within one or more housings orseparated and/or duplicated in different housings. Similarly, any of thecomponents discussed in FIGS. 11-13 may be duplicated more than once.Moreover, various components and modules may be adapted to replaceanother component or module to perform the intended function.

It should also be appreciated that arty of the components/modulespresent in FIGS. 11-13 may be in direct or indirect communication withany of the other components/modules.

It should be appreciated that the healthcare provide computer module asdepicted in FIGS. 11-13 may be any location, person, staff, physician,caregiver, system, device or equipment at any healthcare provider,hospital, clinic, university, vehicle, trailer, or home, as well as anyother location, premises, or organization as desired or required.

It should be appreciated that as discussed herein, a patient or subjectmay be a human or any animal. It should be appreciated that an animalmay be a variety of any applicable type, including, but not limitedthereto, mammal, veterinarian animal, livestock animal or pet typeanimal, etc. As an example, the animal may be a laboratory animalspecifically selected to have certain characteristics similar to human(e.g. rat, dog, pig, monkey), etc. It should be appreciated that thesubject may be any applicable human patient, for example. The patient orsubject may be applicable for, but not limited thereto, any desired orrequired treatment, study, diagnosis, monitoring, tracking, therapy orcare.

FIG. 14 is a functional block diagram for a computer system 1400 forimplementation of an exemplary embodiment or portion of an embodiment ofpresent invention. For example, a method or system of an embodiment ofthe present invention may be implemented using hardware, software or acombination thereof and may be implemented in one or more computersystems or other processing systems, such as personal digit assistants(PDAs), personal computer, laptop, netbook, network, or the likeequipped with adequate memory and processing capabilities. In an exampleembodiment, the invention was implemented in software running on ageneral purpose computer as illustrated in FIG. 14. The computer system1400 may includes one or more processors, such as processor 1404. TheProcessor 1404 is connected to a communication infrastructure 1406(e.g., a communications bus, cross-over bar, or network). The computersystem 1400 may include a display interface 1402 that forwards graphics,text, and/or other data from the communication infrastructure 1406 (orfrom a frame buffer not shown) for display on the display unit 1430.Display unit 1430 may be digital and/or analog.

The computer system 1400 may also include a main memory 1408, preferablyrandom access memory (RAM), and may also include a secondary memory1410. The secondary memory 1410 may include, for example, a hard diskdrive 1412 and/or a removable storage drive 1414, representing a floppydisk drive, a magnetic tape drive, an optical disk drive, a flashmemory, etc. The removable storage drive 1414 reads from and/or writesto a removable storage unit 1418 in a well known manner. Removablestorage unit 1418, represents a floppy disk, magnetic tape, opticaldisk, etc. which is read by and written to by removable storage drive1414. As will be appreciated, the removable storage unit 1418 includes acomputer usable storage medium having stored therein computer softwareand/or data.

In alternative embodiments, secondary memory 1410 may include othermeans for allowing computer programs or other instructions to be loadedinto computer system 1400. Such means may include, for example, aremovable storage unit 1422 and an interface 1420. Examples of suchremovable storage units/interfaces include a program cartridge andcartridge interface (such as that found in video game devices), aremovable memory chip (such as a ROM, PROM, EPROM or EEPROM) andassociated socket, and other removable storage units 1422 and interfaces1420 which allow software and data to be transferred from the removablestorage unit 1422 to computer system 1400.

The computer system 1400 may also include a communications interface1424. Communications interface 1424 allows software and data to betransferred between computer system 1400 and external devices. Examplesof communications interface 1424 may include a modem, a networkinterface (such as an Ethernet card), a communications port (e.g.,serial or parallel, etc.), a PCMCIA slot and card, a modem, etc.Software and data transferred via communications interface 1424 are inthe fibrin of signals 1428 which may be electronic, electromagnetic,optical or other signals capable of being received by communicationsinterface 1424. Signals 1428 are provided to communications interface1424 via a communications path (i.e., channel) 1426. Channel 1426 (orany other communication means or channel disclosed herein) carriessignals 1428 and may be implemented using wire or cable, fiber optics,blue tooth, a phone line, a cellular phone link, an RF link, an infraredlink, wireless link or connection and other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media or medium such asvarious software, firmware, disks, drives, removable storage drive 1414,a hard disk installed in hard disk drive 1412, and signals 1428. Thesecomputer program products (“computer program medium” and “computerusable medium”) are means for providing software to computer system1400. The computer program product may comprise a computer useablemedium having computer program logic thereon. The invention includessuch computer program products. The “computer program product” and“computer useable medium” may be any computer readable medium havingcomputer logic thereon.

Computer programs (also called computer control logic or computerprogram logic) are may be stored in main memory 1408 and/or secondarymemory 1410. Computer programs may also be received via communicationsinterface 1424. Such computer programs, when executed, enable computersystem 1400 to perform the features of the present invention asdiscussed herein. In particular, the computer programs, when executed,enable processor 1404 to perform the functions of the present invention.Accordingly, such computer programs represent controllers of computersystem 1400.

In an embodiment where the invention is implemented using software, thesoftware may be stored in a computer program product and loaded intocomputer system 1400 using removable storage drive 1414, hard drive 1412or communications interface 1424. The control logic (software orcomputer program logic), when executed by the processor 1404, causes theprocessor 1404 to perform the functions of the invention as describedherein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine to perform the functions described herein will be apparentto persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

In an example software embodiment of the invention, the methodsdescribed above may be implemented in SPSS control language or C++programming language, but could be implemented in other variousprograms, computer simulation and computer-aided design, computersimulation environment, MATLAB, or any other software platform orprogram, windows interface or operating system (or other operatingsystem) or other programs known or available to those skilled in theart.

Unless defined otherwise, all technical terms used herein have the samemeanings as commonly understood by one of ordinary skill in the art oftreating diabetes. Specific methods, devices, and materials aredescribed in this application, but any methods and materials similar orequivalent to those described herein can be used in the practice of thepresent invention. While embodiments of the invention have beendescribed in some detail and by way of exemplary illustrations, suchillustration is for purposes of clarity of understanding only, and isnot intended to be limiting. Various terms have been used in thedescription to convey an understanding of the invention; it will beunderstood that the meaning of these various terms extends to commonlinguistic or grammatical variations or forms thereof. It will also beunderstood that when terminology referring to devices, equipment, ordrugs has used trade names, brand names, or common names, that thesenames are provided as contemporary examples, and the invention is notlimited by such literal scope. Terminology that is introduced at a laterdate that may be reasonably understood as a derivative of a contemporaryterm or designating of a subset of objects embraced by a contemporaryterm will be understood as having been described by the now contemporaryterminology. Further, while some theoretical considerations have beenadvanced in furtherance of providing an understanding, for example, ofthe quantitative interrelationships among carbohydrate consumption,glucose levels, and insulin levels, the claims to the invention are notbound by such theory. Moreover, any one or more features of anyembodiment of the invention can be combined with any one or more otherfeatures of any other embodiment of the invention, without departingfrom the scope of the invention. Still further, it should be understoodthat the invention is not limited to the embodiments that have been setforth for purposes of exemplification, but is to be defined only by afair reading of claims that are appended to the patent application,including the full range of equivalency to which each element thereof isentitled.

Unless clearly specified to the contrary, there is no requirement forany particular described or illustrated activity or element, anyparticular sequence or such activities, any particular size, speed,material, duration, contour, dimension or frequency, or any particularlyinterrelationship of such elements. Moreover, any activity can berepeated, any activity can be performed by multiple entities, and/or anyelement can be duplicated. Further, any activity or element can beexcluded, the sequence of activities can vary, and/or theinterrelationship of elements can vary. It should be appreciated thataspects of the present invention may have a variety of sizes, contours,shapes, compositions and materials as desired or required.

In summary, while the present invention has been described with respectto specific embodiments, many modifications, variations, alterations,substitutions, and equivalents will be apparent to those skilled in theart. The present invention is not to be limited in scope by the specificembodiment described herein. Indeed, various modifications of thepresent invention, in addition to those described herein, will beapparent to those of skill in the art from the foregoing description andaccompanying drawings. Accord ugly, the invention is to be considered aslimited only by the spirit and scope of the following claims, includingall modifications and equivalents.

Still other embodiments will become readily apparent to those skilled inthis art from reading the above-recited detailed description anddrawings of certain exemplary embodiments. It should be understood thatnumerous variations, modifications, and additional embodiments arepossible, and accordingly, all such variations, modifications, andembodiments are to be regarded as being within the spirit and scope ofthis application. For example, regardless of the content of any portion(e.g., title, field, background, summary, abstract, drawing figure,etc.) of this application, unless clearly specified to the contrary,there is no requirement for the inclusion in any claim herein or of anyapplication claiming priority hereto of any particular described orillustrated activity or element, any particular sequence of suchactivities, or any particular interrelationship of such elements.Moreover, any activity can be repeated, any activity can be performed bymultiple entities, and/or any element can be duplicated. Further, anyactivity or element can be excluded, the sequence of activities canvary, and/or the interrelationship of elements can vary. Unless clearlyspecified to the contrary, there is no requirement for any particulardescribed or illustrated activity or element, any particular sequence orsuch activities, any particular size, speed, material, dimension orfrequency, or any particularly interrelationship of such elements.Accordingly, the descriptions and drawings are to be regarded asillustrative in nature, and not as restrictive. Moreover, when anynumber or range is described herein, unless clearly stated otherwise,that number or range is approximate. When any range is described herein,unless clearly stated otherwise, that range includes all values thereinand all sub ranges therein. Any information in any material (e.g., aUnited States/foreign patent, United States/foreign patent application,book, article, etc.) that has been incorporated by reference herein, isonly incorporated by reference to the extent that no conflict existsbetween such information and the other statements and drawings set forthherein. In the event of such conflict, including a conflict that wouldrender invalid any claim herein or seeking priority hereto, then anysuch conflicting information in such incorporated by reference materialis specifically not incorporated by reference herein.

REFERENCES

The devices, systems, computer program products, and methods of variousembodiments of the invention disclosed herein may utilize aspectsdisclosed in the following references, applications, publications andpatents and which are hereby incorporated by reference herein in theirentirety:

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1-213. (canceled)
 214. A computer-implemented method for preventing ormitigating hypoglycemia in a subject, comprising: receiving metabolicmeasurements associated with the subject from a metabolic measurementdevice; assessing a risk of hypoglycemia based on the received metabolicmeasurements; determining an insulin delivery attenuation factor basedon the assessed risk of hypoglycemia; and outputting said insulindelivery attenuation factor to an insulin delivery device that adjustsdelivery of insulin to said subject in accordance with said insulindelivery attenuation factor, if the assessed risk of hypoglycemia isabove a predetermined threshold.
 215. The method of claim 214, whereinsaid metabolic measurement comprises blood or interstitial glucose data.216. The method of claim 214, wherein said risk assessment is determinedby a risk assessment function R(t), where:${R(t)} = {\frac{1}{M}{\sum\limits_{\tau = 0}^{M - 1}{\overset{\sim}{R}\left( {t - \tau} \right)}}}$where M is a size of a moving average window for the risk assessmentand, for any stage t, the raw risk value {tilde over (R)}(t) is computedas ${\overset{\sim}{R}(t)},{= \left\{ \begin{matrix}{10 \cdot \left\lbrack {{\gamma (\theta)} \cdot \left( {{\ln \left( {y(t)} \right)}^{\alpha {(\theta)}} - {\beta (\theta)}} \right)} \right\rbrack^{2}} & {{{if}\mspace{14mu} 20} < {y(t)} < \theta} \\100 & {{{if}\mspace{14mu} {y(t)}} = 20} \\0 & {{otherwise},}\end{matrix} \right.}$ and wherein the parameters α(θ), β(θ), and γ(θ)are computed in advance based on a threshold glucose concentration, θ,mg/dl.
 217. The method of claim 216, wherein the threshold glucoseconcentration is the glucose concentration below which the riskassessment function will be positive.
 218. The method of claim 216,wherein the values for parameters α(θ), β(θ), and γ(θ) are as follows:Threshold Glucose Concentration θ (mg/dl) α(θ) β(θ) γ(θ) 90 0.3840551.78181 12.2688 100 0.712949 2.97071 4.03173 112.5 1.08405 5.381 1.5088120 1.29286 7.57332 0.918642 160 2.29837 41.8203 0.10767 200 3.24386223.357 0.0168006


219. The method of claim 216, wherein the insulin delivery attenuationfactor is φ_(brakes)(R(t)), whereφ_(brakes)(R(t))=1/(1+k·R(t)), and wherein k is an aggressivenessfactor.
 220. The method of claim 219, wherein the aggressiveness factorcorresponds to insulin sensitivity.
 221. The method of claim 219,wherein the aggressiveness factor k is:k=exp(−0.7672−0.0091·TDI+0.0449·CF), wherein TDI is total daily insulinand CF is a correction factor.
 222. The method of claim 216, furthercomprising: obtaining a programmed rate of insulin injection; andmultiplying the programmed rate of insulin injection by the insulindelivery attenuation factor to determine an attenuated insulin injectionrate.
 223. A system for preventing or mitigating hypoglycemia in asubject, comprising: a metabolic measurement device for obtainingmetabolic measurements associated with the subject; an assessment devicefor receiving said metabolic measurements and assessing a risk ofhypoglycemia based on the metabolic measurements; an evaluation devicefor determining an insulin delivery attenuation factor based on theassessed the risk of hypoglycemia, and outputting said insulin deliveryattenuation factor to an insulin delivery device that adjusts deliveryof insulin to said subject in accordance with said insulin deliveryattenuation factor, if the assessed risk of hypoglycemia is above apredetermined threshold.
 224. The system of claim 223, wherein saidmetabolic measurement comprises blood or interstitial glucose data. 225.The system of claim 223, wherein the risk assessment is determined by arisk assessment function R(t), where:${R(t)} = {\frac{1}{M}{\sum\limits_{\tau = 0}^{M - 1}{\overset{\sim}{R}\left( {t - \tau} \right)}}}$where M is a size of a moving average window for the risk assessmentfunction and, for any stage t, the raw risk value {tilde over (R)}(t) iscomputed as ${\overset{\sim}{R}(t)},{= \left\{ \begin{matrix}{10 \cdot \left\lbrack {{\gamma (\theta)} \cdot \left( {{\ln \left( {y(t)} \right)}^{\alpha {(\theta)}} - {\beta (\theta)}} \right)} \right\rbrack^{2}} & {{{if}\mspace{14mu} 20} < {y(t)} < \theta} \\100 & {{{if}\mspace{14mu} {y(t)}} = 20} \\0 & {{otherwise},}\end{matrix} \right.}$ and wherein the parameters α(θ), β(θ), and γ(θ)are computed in advance based on a threshold glucose concentration, θ,mg/dl.
 226. The system of claim 225, wherein the threshold glucoseconcentration is the glucose concentration below which the risk functionwill be positive.
 227. The system of claim 225, wherein the values forparameters α(θ), β(θ), and γ(θ) are as follows: Threshold GlucoseConcentration θ (mg/dl) α(θ) β(θ) γ(θ) 90 0.384055 1.78181 12.2688 1000.712949 2.97071 4.03173 112.5 1.08405 5.381 1.5088 120 1.29286 7.573320.918642 160 2.29837 41.8203 0.10767 200 3.24386 223.357 0.0168006


228. The system of claim 223, wherein the insulin delivery attenuationfactor is φ_(brakes)(R(t)), whereφ_(brakes)(R(t))=1/(1+k·R(t)), and wherein k is an aggressivenessfactor.
 229. The system of claim 228, wherein the aggressiveness factorcorresponds to insulin sensitivity.
 230. The system of claim 228,wherein the aggressiveness factor k is:k=exp(−0.7672−0.0091·TDI+0.0449·CF), wherein TDI is total daily insulinand CF is a correction factor.
 231. The system of claim 228, furthercomprising: a second obtaining device for obtaining a programmed rate ofinsulin injection; and a multiplication device for multiplying theprogrammed rate of insulin injection by the insulin delivery attenuationfactor to determine an attenuated insulin injection rate.
 232. Acomputer program product comprising a non-transitory computer readablestorage medium having stored therein computer executable instructionsfor causing a computer system to prevent or mitigate hypoglycemia in asubject, said computer executable instructions comprising instructionsfor: receiving data of metabolic measurements associated with thesubject from a metabolic measurement device; assessing a risk ofhypoglycemia based on the received metabolic measurements; determiningan insulin delivery attenuation factor based on the assessed risk ofhypoglycemia; and outputting said insulin delivery attenuation factor toan insulin delivery device that adjusts delivery of insulin to saidsubject in accordance with said insulin delivery attenuation factor, ifthe assessed risk of hypoglycemia is above a predetermined threshold.233. The computer program product of claim 232, wherein said metabolicmeasurement comprises blood or interstitial glucose data.
 234. Thecomputer program product of claim 232, wherein the risk assessment isdetermined by a risk assessment function R(t), where:${R(t)} = {\frac{1}{M}{\sum\limits_{\tau = 0}^{M - 1}{\overset{\sim}{R}\left( {t - \tau} \right)}}}$where M is a size of a moving average window for the risk assessmentand, for any stage t, the raw risk value {tilde over (R)}(t) is computedas ${\overset{\sim}{R}(t)},{= \left\{ \begin{matrix}{10 \cdot \left\lbrack {{\gamma (\theta)} \cdot \left( {{\ln \left( {y(t)} \right)}^{\alpha {(\theta)}} - {\beta (\theta)}} \right)} \right\rbrack^{2}} & {{{if}\mspace{14mu} 20} < {y(t)} < \theta} \\100 & {{{if}\mspace{14mu} {y(t)}} = 20} \\0 & {{otherwise},}\end{matrix} \right.}$ and wherein the parameters α(θ), β(θ), and γ(θ)are computed in advance based on a threshold glucose concentration, θ,mg/dl.
 235. The computer program product of claim 234, wherein thevalues for parameters α(θ), β(θ), and γ(θ) are as follows: ThresholdGlucose Concentration θ (mg/dl) α(θ) β(θ) γ(θ) 90 0.384055 1.7818112.2688 100 0.712949 2.97071 4.03173 112.5 1.08405 5.381 1.5088 1201.29286 7.57332 0.918642 160 2.29837 41.8203 0.10767 200 3.24386 223.3570.0168006


236. The computer program product of claim 234, wherein the insulindelivery attenuation factor is φ_(brakes)(R(t)), whereφ_(brakes)(R(t))=1/(1+k·R(t)), and wherein k is an aggressivenessfactor.
 237. The computer program product of claim 236, wherein theaggressiveness factor k isk=exp(−0.7672−0.0091·TDI+0.0449·CF), wherein TDI is total daily insulinand CF is a correction factor.
 238. The method of claim 214, furthercomprising: receiving external insulin data associated with saidsubject; whereby said risk assessment is determined by using saidreceived external insulin data in addition to said received metabolicmeasurements.
 239. The system of claim 223, wherein said assessmentdevice further receives external insulin data associated with saidsubject; whereby said risk assessment is determined by using saidreceived external insulin data in addition to said received metabolicmeasurements.
 240. The computer program product of claim 232, furthercomprising computer executable instructions for: receiving externalinsulin data associated with said subject; and wherein said instructionsfor assessing risk use said received external insulin data in additionto said received metabolic measurements.
 241. The method of claim 214,further comprising: outputting a signal to a user corresponding to adetermined one of a plurality of predefined levels of hypoglycemia riskas determined based on the received metabolic measurements, wherein oneof said levels corresponds to a signal indicating that externalintervention is needed to reduce risk of hypoglycemia.
 242. The systemof claim 223, wherein the assessment device further is adapted to outputa signal to a user corresponding to a determined one of a plurality ofpredefined levels of hypoglycemia risk as determined based on thereceived metabolic measurements, wherein one of said levels correspondsto a signal indicating that external intervention is needed to reducerisk of hypoglycemia.
 243. The computer program product of claim 232,further comprising computer executable instructions for: outputting asignal to a user corresponding to a determined one of a plurality ofpredefined levels of hypoglycemia risk as determined based on thereceived metabolic measurements, wherein one of said levels correspondsto a signal indicating that external intervention is needed to reducerisk of hypoglycemia.